用大模型让智能家居聊天机器人懂用户实时状态，更贴心。
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models
- 结合超宽带定位与传感器数据，实时感知用户位置和活动
- 基于上下文生成个性化建议，交互比传统静态聊天更智能
- 适合智能家居、人机交互研究者关注
本文提出一种新型架构，利用大语言模型（LLM）提升智能环境中的上下文感知交互体验。系统通过超宽带（UWB）标签和带传感器的智能家居获取用户位置数据，并结合实时人体活动识别（HAR），全面理解用户上下文。该上下文信息被输入至基于LLM的聊天机器人，使其能根据用户的当前活动与环境生成个性化互动与建议。相比传统静态交互，本方法可动态适应用户实时情境。基于真实世界数据集的案例研究验证了该架构的可行性与有效性，表明融合LLM与实时活动及位置数据，能显著提升交互的个性化与情境相关性。
原文摘要 · Abstract (English)
This work presents a novel architecture for context-aware interactions within smart environments, leveraging Large Language Models (LLMs) to enhance user experiences. Our system integrates user location data obtained through UWB tags and sensor-equipped smart homes with real-time human activity recognition (HAR) to provide a comprehensive understanding of user context. This contextual information is then fed to an LLM-powered chatbot, enabling it to generate personalised interactions and recommendations based on the user's current activity and environment. This approach moves beyond traditional static chatbot interactions by dynamically adapting to the user's real-time situation. A case study conducted from a real-world dataset demonstrates the feasibility and effectiveness of our proposed architecture, showcasing its potential to create more intuitive and helpful interactions within smart homes. The results highlight the significant benefits of integrating LLM with real-time activity and location data to deliver personalised and contextually relevant user experiences.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。